Methodology
The GlobMaps Drought Severity Index (MDI v2.2) combines four complementary data sources to produce a single, reliable severity score calibrated for Southeast Asian conditions.
Vietnam uses higher VCI weight (25%) to better represent Central Highlands coffee/rubber agriculture. Highlighted weights differ from THA/MYS defaults.
These are GlobMaps SPEI-3 bands, not USDM classes. A province’s D0–D4 class comes from its own thresholds (table below); one band spans more than one class, so the two do not convert into each other.
MDI v2.2 reflects atmospheric and soil moisture deficits, plus vegetation stress. For water supply planning, consult national hydrology agencies (TMD, NCHMF, JPS).
This index is decision-support, not a directive: use it as one input among many, never as the sole basis for decisions affecting water supply, crops, livelihoods, or financial exposure.
Sepulcre-Canto et al. (2012) — EU Combined Drought Indicator (CDI)
Anderson et al. (2011) — Evaporative Stress Index (ESI)
Vicente-Serrano et al. (2010) — SPEI: Multiscalar Drought Index
Liu et al. (2015) — Vegetation Condition Index (VCI) for drought monitoring
Muñoz-Sabater et al. (2021) — ERA5-Land: global reanalysis dataset
Our drought figures are computed from source files that are periodically recomputed as inputs are reprocessed. When a recomputation changes a figure we have already published, we correct it and record the change here rather than revising silently.
May 2026 revision (13 August 2026). The May 2026 country figures were revised after the underlying drought files were recomputed on 9 August 2026. A month-scoped re-read (verification runs 31711356352 and 32024730295, the second run under a check that requires every probe to report that it ran) returns Thailand −0.376 / 37.9%, Vietnam −0.504 / 51.1%, Malaysia −0.327 / 39.0%. Earlier published values were −0.288, −0.487 / 46.6% and −0.398 / 34.4%. The grid-cell totals (718, 1,132, 698) are unchanged. One aspect remains open: Malaysia’s mean moved towards wetter while its share of cells past the −0.5 threshold rose, which means the distribution changed shape and not only its centre; we are publishing the verified figures together with that open question.
Drought Risk Patterns in Southeast Asia 2000-2025: Province-Level Analysis Using ERA5 Reanalysis and a Multi-Index Composite Methodology
Yodsri, W. (2026). GlobMaps Climate Intelligence. CC BY 4.0.
https://doi.org/10.5281/zenodo.20774311Peer-review preprint describing the open data backbone (ERA5, MODIS, ALEXI ESI) and the descriptive, province-level regional analysis underlying the GlobMaps drought methodology. Proprietary composite parameters are not disclosed in the preprint.
The component names, relative weights, and data sources shown above are disclosed for transparency and regulatory compliance purposes. The underlying algorithms, calibration coefficients, interpolation procedures, ensemble downscaling methods, validation datasets, and implementation parameters are proprietary trade secrets of GlobMaps. No inference about these proprietary elements should be drawn from the information disclosed herein.
MDI v2.2 · Updated monthly · GlobMaps Climate Intelligence Platform